Overview

Brought to you by YData

Dataset statistics

Number of variables31
Number of observations346018
Missing cells1565549
Missing cells (%)14.6%
Duplicate rows1302
Duplicate rows (%)0.4%
Total size in memory81.8 MiB
Average record size in memory248.0 B

Variable types

DateTime1
Text18
Categorical5
Numeric6
Boolean1

Alerts

Dataset has 1302 (0.4%) duplicate rowsDuplicates
Sub-Acquisition Method is highly imbalanced (51.4%) Imbalance
CalCard is highly imbalanced (88.4%) Imbalance
Purchase Date has 17436 (5.0%) missing values Missing
LPA Number has 253673 (73.3%) missing values Missing
Requisition Number has 331649 (95.8%) missing values Missing
Sub-Acquisition Type has 277681 (80.3%) missing values Missing
Sub-Acquisition Method has 315122 (91.1%) missing values Missing
Supplier Qualifications has 204273 (59.0%) missing values Missing
Supplier Zip Code has 70110 (20.3%) missing values Missing
Location has 70110 (20.3%) missing values Missing
Supplier Code is highly skewed (γ1 = 343.4821729) Skewed
Quantity is highly skewed (γ1 = 117.4482872) Skewed
Supplier Code has 4473 (1.3%) zeros Zeros

Reproduction

Analysis started2024-12-15 13:19:07.417860
Analysis finished2024-12-15 13:19:54.971040
Duration47.55 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Distinct1015
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
Minimum2012-07-02 00:00:00
Maximum2015-06-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2024-12-15T15:19:55.188222image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:55.394861image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Purchase Date
Text

Missing 

Distinct2268
Distinct (%)0.7%
Missing17436
Missing (%)5.0%
Memory size2.6 MiB
2024-12-15T15:19:55.798078image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters3285820
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique328 ?
Unique (%)0.1%

Sample

1st row06/05/2014
2nd row03/12/2014
3rd row10/01/2014
4th row04/14/2014
5th row07/26/2013
ValueCountFrequency (%)
07/01/2014 4499
 
1.4%
07/01/2013 4462
 
1.4%
07/01/2015 2755
 
0.8%
07/01/2012 2218
 
0.7%
05/01/2013 1221
 
0.4%
05/27/2014 1126
 
0.3%
06/05/2013 1117
 
0.3%
09/25/2013 1072
 
0.3%
03/25/2014 1018
 
0.3%
06/30/2015 1006
 
0.3%
Other values (2258) 308088
93.8%
2024-12-15T15:19:56.371655image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 754765
23.0%
/ 657164
20.0%
1 597757
18.2%
2 559696
17.0%
3 180341
 
5.5%
4 164978
 
5.0%
5 120727
 
3.7%
6 68665
 
2.1%
7 64861
 
2.0%
9 59012
 
1.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3285820
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 754765
23.0%
/ 657164
20.0%
1 597757
18.2%
2 559696
17.0%
3 180341
 
5.5%
4 164978
 
5.0%
5 120727
 
3.7%
6 68665
 
2.1%
7 64861
 
2.0%
9 59012
 
1.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3285820
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 754765
23.0%
/ 657164
20.0%
1 597757
18.2%
2 559696
17.0%
3 180341
 
5.5%
4 164978
 
5.0%
5 120727
 
3.7%
6 68665
 
2.1%
7 64861
 
2.0%
9 59012
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3285820
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 754765
23.0%
/ 657164
20.0%
1 597757
18.2%
2 559696
17.0%
3 180341
 
5.5%
4 164978
 
5.0%
5 120727
 
3.7%
6 68665
 
2.1%
7 64861
 
2.0%
9 59012
 
1.8%

Fiscal Year
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
2013-2014
120636 
2014-2015
116537 
2012-2013
108845 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters3114162
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013-2014
2nd row2013-2014
3rd row2013-2014
4th row2013-2014
5th row2013-2014

Common Values

ValueCountFrequency (%)
2013-2014 120636
34.9%
2014-2015 116537
33.7%
2012-2013 108845
31.5%

Length

2024-12-15T15:19:56.530501image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-15T15:19:56.665395image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
2013-2014 120636
34.9%
2014-2015 116537
33.7%
2012-2013 108845
31.5%

Most occurring characters

ValueCountFrequency (%)
2 800881
25.7%
0 692036
22.2%
1 692036
22.2%
- 346018
11.1%
4 237173
 
7.6%
3 229481
 
7.4%
5 116537
 
3.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3114162
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 800881
25.7%
0 692036
22.2%
1 692036
22.2%
- 346018
11.1%
4 237173
 
7.6%
3 229481
 
7.4%
5 116537
 
3.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3114162
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 800881
25.7%
0 692036
22.2%
1 692036
22.2%
- 346018
11.1%
4 237173
 
7.6%
3 229481
 
7.4%
5 116537
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3114162
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 800881
25.7%
0 692036
22.2%
1 692036
22.2%
- 346018
11.1%
4 237173
 
7.6%
3 229481
 
7.4%
5 116537
 
3.7%

LPA Number
Text

Missing 

Distinct1420
Distinct (%)1.5%
Missing253673
Missing (%)73.3%
Memory size2.6 MiB
2024-12-15T15:19:56.949323image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length16
Median length15
Mean length11.036104
Min length2

Characters and Unicode

Total characters1019129
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique399 ?
Unique (%)0.4%

Sample

1st row7-12-70-26
2nd row1-10-75-60A
3rd row1-14-75-60A
4th row1-14-75-60A
5th rowSBP01337
ValueCountFrequency (%)
7-11-51-02 9267
 
8.9%
4105
 
3.9%
1-10-75-60a 3763
 
3.6%
1-12-65-65-01-e 3717
 
3.6%
a 3243
 
3.1%
g 3229
 
3.1%
1-11-70-04o 3097
 
3.0%
1-13-70-02a 2612
 
2.5%
1-11-70-04q 2481
 
2.4%
1-13-70-01a 2098
 
2.0%
Other values (1415) 67064
64.1%
2024-12-15T15:19:57.409798image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 281197
27.6%
1 210038
20.6%
0 125737
12.3%
7 62873
 
6.2%
5 52827
 
5.2%
2 50578
 
5.0%
3 40364
 
4.0%
9 31213
 
3.1%
4 30075
 
3.0%
6 29901
 
2.9%
Other values (34) 104326
 
10.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1019129
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
- 281197
27.6%
1 210038
20.6%
0 125737
12.3%
7 62873
 
6.2%
5 52827
 
5.2%
2 50578
 
5.0%
3 40364
 
4.0%
9 31213
 
3.1%
4 30075
 
3.0%
6 29901
 
2.9%
Other values (34) 104326
 
10.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1019129
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
- 281197
27.6%
1 210038
20.6%
0 125737
12.3%
7 62873
 
6.2%
5 52827
 
5.2%
2 50578
 
5.0%
3 40364
 
4.0%
9 31213
 
3.1%
4 30075
 
3.0%
6 29901
 
2.9%
Other values (34) 104326
 
10.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1019129
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
- 281197
27.6%
1 210038
20.6%
0 125737
12.3%
7 62873
 
6.2%
5 52827
 
5.2%
2 50578
 
5.0%
3 40364
 
4.0%
9 31213
 
3.1%
4 30075
 
3.0%
6 29901
 
2.9%
Other values (34) 104326
 
10.2%
Distinct200533
Distinct (%)58.0%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
2024-12-15T15:19:57.815506image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length32
Median length31
Mean length8.969938
Min length1

Characters and Unicode

Total characters3103760
Distinct characters82
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique166350 ?
Unique (%)48.1%

Sample

1st rowREQ0011118
2nd rowREQ0011932
3rd rowREQ0011476
4th row4500236642
5th row4500221028
ValueCountFrequency (%)
2660 1393
 
0.4%
prf 930
 
0.3%
boe 631
 
0.2%
4500211314 602
 
0.2%
4500201426 579
 
0.2%
4500204899 578
 
0.2%
4500203794 578
 
0.2%
4500202454 564
 
0.2%
4500198630 547
 
0.2%
4500210216 546
 
0.2%
Other values (199482) 346489
98.0%
2024-12-15T15:19:58.402884image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 664769
21.4%
1 386930
12.5%
4 322860
10.4%
2 295105
9.5%
5 282763
9.1%
3 215362
 
6.9%
6 152176
 
4.9%
7 133355
 
4.3%
8 131526
 
4.2%
9 124993
 
4.0%
Other values (72) 393921
12.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3103760
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 664769
21.4%
1 386930
12.5%
4 322860
10.4%
2 295105
9.5%
5 282763
9.1%
3 215362
 
6.9%
6 152176
 
4.9%
7 133355
 
4.3%
8 131526
 
4.2%
9 124993
 
4.0%
Other values (72) 393921
12.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3103760
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 664769
21.4%
1 386930
12.5%
4 322860
10.4%
2 295105
9.5%
5 282763
9.1%
3 215362
 
6.9%
6 152176
 
4.9%
7 133355
 
4.3%
8 131526
 
4.2%
9 124993
 
4.0%
Other values (72) 393921
12.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3103760
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 664769
21.4%
1 386930
12.5%
4 322860
10.4%
2 295105
9.5%
5 282763
9.1%
3 215362
 
6.9%
6 152176
 
4.9%
7 133355
 
4.3%
8 131526
 
4.2%
9 124993
 
4.0%
Other values (72) 393921
12.7%

Requisition Number
Text

Missing 

Distinct5997
Distinct (%)41.7%
Missing331649
Missing (%)95.8%
Memory size2.6 MiB
2024-12-15T15:19:58.711859image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length11
Median length10
Mean length9.9869859
Min length4

Characters and Unicode

Total characters143503
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4023 ?
Unique (%)28.0%

Sample

1st rowREQ0011118
2nd rowREQ0011932
3rd rowREQ0011476
4th rowREQ0013911
5th rowREQ0014515
ValueCountFrequency (%)
req0008872 123
 
0.9%
req0010655 81
 
0.6%
req0009177 65
 
0.5%
req0010201 62
 
0.4%
req0008985 59
 
0.4%
req0008405 55
 
0.4%
req0010705 51
 
0.4%
req0009336 51
 
0.4%
req0010418 49
 
0.3%
req0010689 39
 
0.3%
Other values (5984) 13734
95.6%
2024-12-15T15:19:59.200789image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 38895
27.1%
1 17527
12.2%
R 14323
 
10.0%
E 14075
 
9.8%
Q 14023
 
9.8%
9 6459
 
4.5%
2 6183
 
4.3%
3 6070
 
4.2%
4 5547
 
3.9%
8 4991
 
3.5%
Other values (31) 15410
 
10.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 143503
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 38895
27.1%
1 17527
12.2%
R 14323
 
10.0%
E 14075
 
9.8%
Q 14023
 
9.8%
9 6459
 
4.5%
2 6183
 
4.3%
3 6070
 
4.2%
4 5547
 
3.9%
8 4991
 
3.5%
Other values (31) 15410
 
10.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 143503
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 38895
27.1%
1 17527
12.2%
R 14323
 
10.0%
E 14075
 
9.8%
Q 14023
 
9.8%
9 6459
 
4.5%
2 6183
 
4.3%
3 6070
 
4.2%
4 5547
 
3.9%
8 4991
 
3.5%
Other values (31) 15410
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 143503
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 38895
27.1%
1 17527
12.2%
R 14323
 
10.0%
E 14075
 
9.8%
Q 14023
 
9.8%
9 6459
 
4.5%
2 6183
 
4.3%
3 6070
 
4.2%
4 5547
 
3.9%
8 4991
 
3.5%
Other values (31) 15410
 
10.7%

Acquisition Type
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
NON-IT Goods
215083 
NON-IT Services
68372 
IT Goods
50900 
IT Services
 
11516
IT Telecommunications
 
147

Length

Max length21
Median length12
Mean length11.974923
Min length8

Characters and Unicode

Total characters4143539
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIT Goods
2nd rowNON-IT Goods
3rd rowIT Services
4th rowNON-IT Goods
5th rowNON-IT Goods

Common Values

ValueCountFrequency (%)
NON-IT Goods 215083
62.2%
NON-IT Services 68372
 
19.8%
IT Goods 50900
 
14.7%
IT Services 11516
 
3.3%
IT Telecommunications 147
 
< 0.1%

Length

2024-12-15T15:19:59.368995image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-15T15:19:59.520982image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
ValueCountFrequency (%)
non-it 283455
41.0%
goods 265983
38.4%
services 79888
 
11.5%
it 62563
 
9.0%
telecommunications 147
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
N 566910
13.7%
o 532260
12.8%
T 346165
8.4%
I 346018
8.4%
346018
8.4%
s 346018
8.4%
O 283455
6.8%
- 283455
6.8%
G 265983
6.4%
d 265983
6.4%
Other values (12) 561274
13.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4143539
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
N 566910
13.7%
o 532260
12.8%
T 346165
8.4%
I 346018
8.4%
346018
8.4%
s 346018
8.4%
O 283455
6.8%
- 283455
6.8%
G 265983
6.4%
d 265983
6.4%
Other values (12) 561274
13.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4143539
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
N 566910
13.7%
o 532260
12.8%
T 346165
8.4%
I 346018
8.4%
346018
8.4%
s 346018
8.4%
O 283455
6.8%
- 283455
6.8%
G 265983
6.4%
d 265983
6.4%
Other values (12) 561274
13.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4143539
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
N 566910
13.7%
o 532260
12.8%
T 346165
8.4%
I 346018
8.4%
346018
8.4%
s 346018
8.4%
O 283455
6.8%
- 283455
6.8%
G 265983
6.4%
d 265983
6.4%
Other values (12) 561274
13.5%

Sub-Acquisition Type
Categorical

Missing 

Distinct25
Distinct (%)< 0.1%
Missing277681
Missing (%)80.3%
Memory size2.6 MiB
Personal Services
16104 
Services are specifically exempt by statute
11854 
Emergency Contract
7913 
Subvention and Local Assistance
7048 
Public Works
4791 
Other values (20)
20627 

Length

Max length67
Median length43
Mean length25.958397
Min length11

Characters and Unicode

Total characters1773919
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPersonal Services
2nd rowPersonal Services
3rd rowLegal Services
4th rowLegal Services
5th rowLegal Services

Common Values

ValueCountFrequency (%)
Personal Services 16104
 
4.7%
Services are specifically exempt by statute 11854
 
3.4%
Emergency Contract 7913
 
2.3%
Subvention and Local Assistance 7048
 
2.0%
Public Works 4791
 
1.4%
Expert Witneses 3800
 
1.1%
Interagency Agreements 3261
 
0.9%
Agreements with other governmental entities and public universities 2051
 
0.6%
Architectural and Engineering 1785
 
0.5%
Contracts with Local Governments 1562
 
0.5%
Other values (15) 8168
 
2.4%
(Missing) 277681
80.3%

Length

2024-12-15T15:19:59.699780image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
services 32201
 
14.4%
personal 16104
 
7.2%
and 12477
 
5.6%
are 11854
 
5.3%
specifically 11854
 
5.3%
exempt 11854
 
5.3%
by 11854
 
5.3%
statute 11854
 
5.3%
local 8610
 
3.9%
emergency 7913
 
3.5%
Other values (51) 87054
38.9%

Most occurring characters

ValueCountFrequency (%)
e 237220
13.4%
155292
 
8.8%
s 127181
 
7.2%
t 125370
 
7.1%
n 115940
 
6.5%
i 115867
 
6.5%
r 115471
 
6.5%
a 105019
 
5.9%
c 103985
 
5.9%
l 65656
 
3.7%
Other values (34) 506918
28.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1773919
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 237220
13.4%
155292
 
8.8%
s 127181
 
7.2%
t 125370
 
7.1%
n 115940
 
6.5%
i 115867
 
6.5%
r 115471
 
6.5%
a 105019
 
5.9%
c 103985
 
5.9%
l 65656
 
3.7%
Other values (34) 506918
28.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1773919
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 237220
13.4%
155292
 
8.8%
s 127181
 
7.2%
t 125370
 
7.1%
n 115940
 
6.5%
i 115867
 
6.5%
r 115471
 
6.5%
a 105019
 
5.9%
c 103985
 
5.9%
l 65656
 
3.7%
Other values (34) 506918
28.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1773919
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 237220
13.4%
155292
 
8.8%
s 127181
 
7.2%
t 125370
 
7.1%
n 115940
 
6.5%
i 115867
 
6.5%
r 115471
 
6.5%
a 105019
 
5.9%
c 103985
 
5.9%
l 65656
 
3.7%
Other values (34) 506918
28.6%
Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
Informal Competitive
82083 
Statewide Contract
63488 
SB/DVBE Option
38500 
Services are specifically exempt by statute
33045 
State Programs
27842 
Other values (15)
101060 

Length

Max length43
Median length42
Mean length20.229682
Min length3

Characters and Unicode

Total characters6999834
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWSCA/Coop
2nd rowInformal Competitive
3rd rowInformal Competitive
4th rowInformal Competitive
5th rowStatewide Contract

Common Values

ValueCountFrequency (%)
Informal Competitive 82083
23.7%
Statewide Contract 63488
18.3%
SB/DVBE Option 38500
11.1%
Services are specifically exempt by statute 33045
9.6%
State Programs 27842
 
8.0%
Fair and Reasonable 25400
 
7.3%
WSCA/Coop 19478
 
5.6%
Formal Competitive 18479
 
5.3%
Services are specifically exempt by policy 11302
 
3.3%
Emergency Purchase 10186
 
2.9%
Other values (10) 16215
 
4.7%

Length

2024-12-15T15:19:59.894743image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
competitive 100562
 
11.5%
informal 82083
 
9.4%
statewide 63488
 
7.3%
contract 63488
 
7.3%
are 44347
 
5.1%
exempt 44347
 
5.1%
specifically 44347
 
5.1%
by 44347
 
5.1%
services 44347
 
5.1%
option 38500
 
4.4%
Other values (32) 304130
34.8%

Most occurring characters

ValueCountFrequency (%)
e 769414
 
11.0%
t 706065
 
10.1%
a 529084
 
7.6%
527968
 
7.5%
i 481038
 
6.9%
o 412281
 
5.9%
r 373842
 
5.3%
m 289832
 
4.1%
p 259213
 
3.7%
n 251937
 
3.6%
Other values (34) 2399160
34.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6999834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 769414
 
11.0%
t 706065
 
10.1%
a 529084
 
7.6%
527968
 
7.5%
i 481038
 
6.9%
o 412281
 
5.9%
r 373842
 
5.3%
m 289832
 
4.1%
p 259213
 
3.7%
n 251937
 
3.6%
Other values (34) 2399160
34.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6999834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 769414
 
11.0%
t 706065
 
10.1%
a 529084
 
7.6%
527968
 
7.5%
i 481038
 
6.9%
o 412281
 
5.9%
r 373842
 
5.3%
m 289832
 
4.1%
p 259213
 
3.7%
n 251937
 
3.6%
Other values (34) 2399160
34.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6999834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 769414
 
11.0%
t 706065
 
10.1%
a 529084
 
7.6%
527968
 
7.5%
i 481038
 
6.9%
o 412281
 
5.9%
r 373842
 
5.3%
m 289832
 
4.1%
p 259213
 
3.7%
n 251937
 
3.6%
Other values (34) 2399160
34.3%

Sub-Acquisition Method
Categorical

Imbalance  Missing 

Distinct16
Distinct (%)0.1%
Missing315122
Missing (%)91.1%
Memory size2.6 MiB
Fleet
14148 
Prison Industry Authority (PIA)
11602 
Only goods and services that meet needs of the State
1810 
Office of State Printing (OSP)
 
812
Interagency Agreement
 
565
Other values (11)
1959 

Length

Max length58
Median length56
Mean length20.272333
Min length5

Characters and Unicode

Total characters626334
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOther
2nd rowPrison Industry Authority (PIA)
3rd rowPrison Industry Authority (PIA)
4th rowPrison Industry Authority (PIA)
5th rowPrison Industry Authority (PIA)

Common Values

ValueCountFrequency (%)
Fleet 14148
 
4.1%
Prison Industry Authority (PIA) 11602
 
3.4%
Only goods and services that meet needs of the State 1810
 
0.5%
Office of State Printing (OSP) 812
 
0.2%
Interagency Agreement 565
 
0.2%
Services are specifically exempt by statute 521
 
0.2%
Other 503
 
0.1%
Emergency acquisition for the protection of the public 334
 
0.1%
A single firm services a geographic region 328
 
0.1%
Contract with other government agency 117
 
< 0.1%
Other values (6) 156
 
< 0.1%
(Missing) 315122
91.1%

Length

2024-12-15T15:20:00.093340image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
fleet 14148
15.1%
authority 11602
12.4%
pia 11602
12.4%
prison 11602
12.4%
industry 11602
12.4%
of 2956
 
3.2%
services 2677
 
2.9%
state 2622
 
2.8%
the 2478
 
2.6%
and 1810
 
1.9%
Other values (50) 20502
21.9%

Most occurring characters

ValueCountFrequency (%)
t 68653
 
11.0%
62705
 
10.0%
e 55519
 
8.9%
r 42991
 
6.9%
n 34477
 
5.5%
s 33452
 
5.3%
i 32734
 
5.2%
o 32392
 
5.2%
y 27164
 
4.3%
P 24828
 
4.0%
Other values (33) 211419
33.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 626334
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 68653
 
11.0%
62705
 
10.0%
e 55519
 
8.9%
r 42991
 
6.9%
n 34477
 
5.5%
s 33452
 
5.3%
i 32734
 
5.2%
o 32392
 
5.2%
y 27164
 
4.3%
P 24828
 
4.0%
Other values (33) 211419
33.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 626334
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 68653
 
11.0%
62705
 
10.0%
e 55519
 
8.9%
r 42991
 
6.9%
n 34477
 
5.5%
s 33452
 
5.3%
i 32734
 
5.2%
o 32392
 
5.2%
y 27164
 
4.3%
P 24828
 
4.0%
Other values (33) 211419
33.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 626334
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 68653
 
11.0%
62705
 
10.0%
e 55519
 
8.9%
r 42991
 
6.9%
n 34477
 
5.5%
s 33452
 
5.3%
i 32734
 
5.2%
o 32392
 
5.2%
y 27164
 
4.3%
P 24828
 
4.0%
Other values (33) 211419
33.8%
Distinct111
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.6 MiB
2024-12-15T15:20:00.447196image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length59
Median length51
Mean length34.360594
Min length17

Characters and Unicode

Total characters11889384
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowConsumer Affairs, Department of
2nd rowConsumer Affairs, Department of
3rd rowConsumer Affairs, Department of
4th rowCorrectional Health Care Services
5th rowCorrections and Rehabilitation, Department of
ValueCountFrequency (%)
of 272314
18.4%
department 265129
17.9%
and 95652
 
6.5%
services 63459
 
4.3%
rehabilitation 59821
 
4.0%
corrections 57557
 
3.9%
health 49030
 
3.3%
care 35715
 
2.4%
resources 33370
 
2.3%
correctional 32250
 
2.2%
Other values (171) 515655
34.8%
2024-12-15T15:20:01.108116image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1355089
 
11.4%
t 1140741
 
9.6%
1133934
 
9.5%
a 932131
 
7.8%
r 907190
 
7.6%
o 849147
 
7.1%
n 769164
 
6.5%
i 659960
 
5.6%
s 390801
 
3.3%
f 359306
 
3.0%
Other values (40) 3391921
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11889384
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1355089
 
11.4%
t 1140741
 
9.6%
1133934
 
9.5%
a 932131
 
7.8%
r 907190
 
7.6%
o 849147
 
7.1%
n 769164
 
6.5%
i 659960
 
5.6%
s 390801
 
3.3%
f 359306
 
3.0%
Other values (40) 3391921
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11889384
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1355089
 
11.4%
t 1140741
 
9.6%
1133934
 
9.5%
a 932131
 
7.8%
r 907190
 
7.6%
o 849147
 
7.1%
n 769164
 
6.5%
i 659960
 
5.6%
s 390801
 
3.3%
f 359306
 
3.0%
Other values (40) 3391921
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11889384
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1355089
 
11.4%
t 1140741
 
9.6%
1133934
 
9.5%
a 932131
 
7.8%
r 907190
 
7.6%
o 849147
 
7.1%
n 769164
 
6.5%
i 659960
 
5.6%
s 390801
 
3.3%
f 359306
 
3.0%
Other values (40) 3391921
28.5%

Supplier Code
Real number (ℝ)

Skewed  Zeros 

Distinct25239
Distinct (%)7.3%
Missing36
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean843434.48
Minimum0
Maximum9.5483178 × 108
Zeros4473
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2024-12-15T15:20:01.344114image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6509
Q127292
median1012531
Q31482180
95-th percentile1757013
Maximum9.5483178 × 108
Range9.5483178 × 108
Interquartile range (IQR)1454888

Descriptive statistics

Standard deviation2461186.2
Coefficient of variation (CV)2.9180526
Kurtosis131365.99
Mean843434.48
Median Absolute Deviation (MAD)738209
Skewness343.48217
Sum2.9181315 × 1011
Variance6.0574375 × 1012
MonotonicityNot monotonic
2024-12-15T15:20:01.567691image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1743406 13756
 
4.0%
1001584 9441
 
2.7%
1065902 8508
 
2.5%
1008361 6991
 
2.0%
1087660 6709
 
1.9%
17224 5979
 
1.7%
1755386 5033
 
1.5%
0 4473
 
1.3%
12341 3983
 
1.2%
1752319 3625
 
1.0%
Other values (25229) 277484
80.2%
ValueCountFrequency (%)
0 4473
1.3%
15 2
 
< 0.1%
39 377
 
0.1%
42 3
 
< 0.1%
45 1
 
< 0.1%
49 1
 
< 0.1%
61 69
 
< 0.1%
89 8
 
< 0.1%
114 1
 
< 0.1%
123 1
 
< 0.1%
ValueCountFrequency (%)
954831781 1
 
< 0.1%
954650312 1
 
< 0.1%
330284668 1
 
< 0.1%
1796774 6
< 0.1%
1796486 8
< 0.1%
1795780 4
 
< 0.1%
1795272 10
< 0.1%
1794552 4
 
< 0.1%
1794147 9
< 0.1%
1793858 1
 
< 0.1%
Distinct24732
Distinct (%)7.1%
Missing36
Missing (%)< 0.1%
Memory size2.6 MiB
2024-12-15T15:20:01.828148image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length80
Median length72
Mean length23.540043
Min length3

Characters and Unicode

Total characters8144431
Distinct characters86
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10435 ?
Unique (%)3.0%

Sample

1st rowPitney Bowes
2nd rowRodea Auto Tech
3rd rowSmile Business Products, Inc
4th rowASHAN INC
5th rowTechnology Integration Group
ValueCountFrequency (%)
inc 124185
 
10.8%
supply 26861
 
2.3%
systems 23263
 
2.0%
20226
 
1.8%
group 14933
 
1.3%
industrial 14889
 
1.3%
of 14740
 
1.3%
office 14500
 
1.3%
fleet 14274
 
1.2%
business 14114
 
1.2%
Other values (18509) 872395
75.6%
2024-12-15T15:20:02.301558image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
808663
 
9.9%
e 514076
 
6.3%
n 495074
 
6.1%
r 394831
 
4.8%
o 393995
 
4.8%
i 381051
 
4.7%
t 377055
 
4.6%
s 345353
 
4.2%
a 324608
 
4.0%
I 295467
 
3.6%
Other values (76) 3814258
46.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8144431
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
808663
 
9.9%
e 514076
 
6.3%
n 495074
 
6.1%
r 394831
 
4.8%
o 393995
 
4.8%
i 381051
 
4.7%
t 377055
 
4.6%
s 345353
 
4.2%
a 324608
 
4.0%
I 295467
 
3.6%
Other values (76) 3814258
46.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8144431
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
808663
 
9.9%
e 514076
 
6.3%
n 495074
 
6.1%
r 394831
 
4.8%
o 393995
 
4.8%
i 381051
 
4.7%
t 377055
 
4.6%
s 345353
 
4.2%
a 324608
 
4.0%
I 295467
 
3.6%
Other values (76) 3814258
46.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8144431
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
808663
 
9.9%
e 514076
 
6.3%
n 495074
 
6.1%
r 394831
 
4.8%
o 393995
 
4.8%
i 381051
 
4.7%
t 377055
 
4.6%
s 345353
 
4.2%
a 324608
 
4.0%
I 295467
 
3.6%
Other values (76) 3814258
46.8%
Distinct278
Distinct (%)0.2%
Missing204273
Missing (%)59.0%
Memory size2.6 MiB
2024-12-15T15:20:02.479560image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length62
Median length57
Mean length12.242287
Min length2

Characters and Unicode

Total characters1735283
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)< 0.1%

Sample

1st rowCA-MB CA-SB
2nd rowCA-MB CA-SB
3rd rowCA-DVBE CA-MB CA-SB CDVBE
4th rowCA-SB
5th rowCA-MB CA-SB
ValueCountFrequency (%)
ca-sb 133608
42.6%
ca-mb 79876
25.5%
ca-sbe 29408
 
9.4%
ca-dvbe 21146
 
6.7%
sb 19399
 
6.2%
cdvbe 11313
 
3.6%
wbe 4519
 
1.4%
sdvosb 3219
 
1.0%
wosb 3003
 
1.0%
mbe 2708
 
0.9%
Other values (6) 5170
 
1.6%
2024-12-15T15:20:02.891147image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
B 312875
18.0%
C 276125
15.9%
A 265092
15.3%
- 264812
15.3%
S 194049
11.2%
171624
9.9%
M 84217
 
4.9%
E 71577
 
4.1%
D 38441
 
2.2%
V 36661
 
2.1%
Other values (8) 19810
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1735283
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
B 312875
18.0%
C 276125
15.9%
A 265092
15.3%
- 264812
15.3%
S 194049
11.2%
171624
9.9%
M 84217
 
4.9%
E 71577
 
4.1%
D 38441
 
2.2%
V 36661
 
2.1%
Other values (8) 19810
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1735283
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
B 312875
18.0%
C 276125
15.9%
A 265092
15.3%
- 264812
15.3%
S 194049
11.2%
171624
9.9%
M 84217
 
4.9%
E 71577
 
4.1%
D 38441
 
2.2%
V 36661
 
2.1%
Other values (8) 19810
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1735283
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
B 312875
18.0%
C 276125
15.9%
A 265092
15.3%
- 264812
15.3%
S 194049
11.2%
171624
9.9%
M 84217
 
4.9%
E 71577
 
4.1%
D 38441
 
2.2%
V 36661
 
2.1%
Other values (8) 19810
 
1.1%

Supplier Zip Code
Text

Missing 

Distinct3993
Distinct (%)1.4%
Missing70110
Missing (%)20.3%
Memory size2.6 MiB
2024-12-15T15:20:03.292873image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length12
Median length5
Mean length5.1999906
Min length3

Characters and Unicode

Total characters1434719
Distinct characters41
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique742 ?
Unique (%)0.3%

Sample

1st row95841
2nd row91436
3rd row95814
4th row97008
5th row95814
ValueCountFrequency (%)
95691 11095
 
4.0%
95814 10921
 
4.0%
95696 8518
 
3.1%
95827 7159
 
2.6%
95841 7008
 
2.5%
73529 6991
 
2.5%
95742 5676
 
2.1%
95811 4779
 
1.7%
93706 4412
 
1.6%
92653 4027
 
1.5%
Other values (4007) 205462
74.4%
2024-12-15T15:20:03.846501image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9 305406
21.3%
5 193463
13.5%
1 149128
10.4%
6 129810
9.0%
0 126572
8.8%
2 118545
 
8.3%
3 110956
 
7.7%
4 105718
 
7.4%
8 93727
 
6.5%
7 88950
 
6.2%
Other values (31) 12444
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1434719
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 305406
21.3%
5 193463
13.5%
1 149128
10.4%
6 129810
9.0%
0 126572
8.8%
2 118545
 
8.3%
3 110956
 
7.7%
4 105718
 
7.4%
8 93727
 
6.5%
7 88950
 
6.2%
Other values (31) 12444
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1434719
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 305406
21.3%
5 193463
13.5%
1 149128
10.4%
6 129810
9.0%
0 126572
8.8%
2 118545
 
8.3%
3 110956
 
7.7%
4 105718
 
7.4%
8 93727
 
6.5%
7 88950
 
6.2%
Other values (31) 12444
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1434719
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 305406
21.3%
5 193463
13.5%
1 149128
10.4%
6 129810
9.0%
0 126572
8.8%
2 118545
 
8.3%
3 110956
 
7.7%
4 105718
 
7.4%
8 93727
 
6.5%
7 88950
 
6.2%
Other values (31) 12444
 
0.9%

CalCard
Boolean

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size338.0 KiB
False
340646 
True
 
5372
ValueCountFrequency (%)
False 340646
98.4%
True 5372
 
1.6%
2024-12-15T15:20:03.988533image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Distinct180029
Distinct (%)52.0%
Missing32
Missing (%)< 0.1%
Memory size2.6 MiB
2024-12-15T15:20:04.485159image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length101
Median length81
Mean length22.547808
Min length1

Characters and Unicode

Total characters7801226
Distinct characters123
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique151218 ?
Unique (%)43.7%

Sample

1st rowUSB
2nd rowTire Disposal
3rd rowLabor
4th rowToner
5th rowHP 35A BLACK TONER
ValueCountFrequency (%)
for 24932
 
2.2%
17809
 
1.5%
services 15568
 
1.3%
equip 13811
 
1.2%
fuel 13340
 
1.2%
and 12349
 
1.1%
supplies 11452
 
1.0%
maintenance 9254
 
0.8%
medical 7579
 
0.7%
hp 7018
 
0.6%
Other values (82436) 1025214
88.5%
2024-12-15T15:20:05.317934image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
815972
 
10.5%
e 573975
 
7.4%
r 378780
 
4.9%
a 370956
 
4.8%
i 356300
 
4.6%
n 319439
 
4.1%
t 317283
 
4.1%
o 304368
 
3.9%
s 253119
 
3.2%
l 229373
 
2.9%
Other values (113) 3881661
49.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7801226
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
815972
 
10.5%
e 573975
 
7.4%
r 378780
 
4.9%
a 370956
 
4.8%
i 356300
 
4.6%
n 319439
 
4.1%
t 317283
 
4.1%
o 304368
 
3.9%
s 253119
 
3.2%
l 229373
 
2.9%
Other values (113) 3881661
49.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7801226
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
815972
 
10.5%
e 573975
 
7.4%
r 378780
 
4.9%
a 370956
 
4.8%
i 356300
 
4.6%
n 319439
 
4.1%
t 317283
 
4.1%
o 304368
 
3.9%
s 253119
 
3.2%
l 229373
 
2.9%
Other values (113) 3881661
49.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7801226
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
815972
 
10.5%
e 573975
 
7.4%
r 378780
 
4.9%
a 370956
 
4.8%
i 356300
 
4.6%
n 319439
 
4.1%
t 317283
 
4.1%
o 304368
 
3.9%
s 253119
 
3.2%
l 229373
 
2.9%
Other values (113) 3881661
49.8%
Distinct219508
Distinct (%)63.5%
Missing202
Missing (%)0.1%
Memory size2.6 MiB
2024-12-15T15:20:05.866933image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length3380
Median length1707
Mean length41.868366
Min length1

Characters and Unicode

Total characters14478751
Distinct characters131
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique195325 ?
Unique (%)56.5%

Sample

1st rowUSB
2nd rowTire Disposal
3rd rowLabor
4th rowToner
5th rowHP 35A BLACK TONER
ValueCountFrequency (%)
for 53923
 
2.5%
and 42931
 
2.0%
the 34091
 
1.6%
to 33522
 
1.6%
29903
 
1.4%
of 24985
 
1.2%
services 20629
 
1.0%
fuel 13921
 
0.6%
equip 13819
 
0.6%
provide 12904
 
0.6%
Other values (139566) 1877652
87.0%
2024-12-15T15:20:06.619465image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1867193
 
12.9%
e 1035864
 
7.2%
a 695956
 
4.8%
r 682223
 
4.7%
i 661077
 
4.6%
t 653680
 
4.5%
o 627712
 
4.3%
n 623075
 
4.3%
s 491782
 
3.4%
l 396589
 
2.7%
Other values (121) 6743600
46.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14478751
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1867193
 
12.9%
e 1035864
 
7.2%
a 695956
 
4.8%
r 682223
 
4.7%
i 661077
 
4.6%
t 653680
 
4.5%
o 627712
 
4.3%
n 623075
 
4.3%
s 491782
 
3.4%
l 396589
 
2.7%
Other values (121) 6743600
46.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14478751
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1867193
 
12.9%
e 1035864
 
7.2%
a 695956
 
4.8%
r 682223
 
4.7%
i 661077
 
4.6%
t 653680
 
4.5%
o 627712
 
4.3%
n 623075
 
4.3%
s 491782
 
3.4%
l 396589
 
2.7%
Other values (121) 6743600
46.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14478751
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1867193
 
12.9%
e 1035864
 
7.2%
a 695956
 
4.8%
r 682223
 
4.7%
i 661077
 
4.6%
t 653680
 
4.5%
o 627712
 
4.3%
n 623075
 
4.3%
s 491782
 
3.4%
l 396589
 
2.7%
Other values (121) 6743600
46.6%

Quantity
Real number (ℝ)

Skewed 

Distinct6131
Distinct (%)1.8%
Missing30
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2316.1537
Minimum0.0001
Maximum20000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2024-12-15T15:20:06.793093image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum0.0001
5-th percentile1
Q11
median1
Q36
95-th percentile775
Maximum20000000
Range20000000
Interquartile range (IQR)5

Descriptive statistics

Standard deviation105825.91
Coefficient of variation (CV)45.690367
Kurtosis16998.127
Mean2316.1537
Median Absolute Deviation (MAD)0
Skewness117.44829
Sum8.0136137 × 108
Variance1.1199123 × 1010
MonotonicityNot monotonic
2024-12-15T15:20:07.010318image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 214019
61.9%
2 18383
 
5.3%
4 8794
 
2.5%
3 8288
 
2.4%
10 7053
 
2.0%
5 6377
 
1.8%
6 5852
 
1.7%
20 4108
 
1.2%
12 3756
 
1.1%
8 3343
 
1.0%
Other values (6121) 66015
 
19.1%
ValueCountFrequency (%)
0.0001 2
 
< 0.1%
0.004 1
 
< 0.1%
0.0055 1
 
< 0.1%
0.01 5
< 0.1%
0.0102 1
 
< 0.1%
0.0113 1
 
< 0.1%
0.1 8
< 0.1%
0.13 1
 
< 0.1%
0.14 1
 
< 0.1%
0.18 1
 
< 0.1%
ValueCountFrequency (%)
20000000 2
< 0.1%
18000000 1
 
< 0.1%
15000000 4
< 0.1%
12000000 2
< 0.1%
10000000 1
 
< 0.1%
9750000 1
 
< 0.1%
9408000 1
 
< 0.1%
9000000 2
< 0.1%
8250000 2
< 0.1%
6545000 1
 
< 0.1%
Distinct128167
Distinct (%)37.0%
Missing30
Missing (%)< 0.1%
Memory size2.6 MiB
2024-12-15T15:20:07.529054image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length14
Median length12
Mean length7.3313005
Min length5

Characters and Unicode

Total characters2536542
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique99844 ?
Unique (%)28.9%

Sample

1st row$1.00
2nd row$2.00
3rd row$150.00
4th row$6080.26
5th row$45.40
ValueCountFrequency (%)
0.00 7552
 
2.2%
1.00 3616
 
1.0%
10000.00 2207
 
0.6%
50000.00 1226
 
0.4%
4.00 1140
 
0.3%
15000.00 1052
 
0.3%
20000.00 946
 
0.3%
30000.00 941
 
0.3%
25000.00 923
 
0.3%
1.75 865
 
0.3%
Other values (127648) 325520
94.1%
2024-12-15T15:20:08.225644image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 560208
22.1%
$ 345988
13.6%
. 345988
13.6%
1 198799
 
7.8%
5 170454
 
6.7%
2 164405
 
6.5%
4 133584
 
5.3%
9 131910
 
5.2%
3 131539
 
5.2%
6 122750
 
4.8%
Other values (3) 230917
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2536542
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 560208
22.1%
$ 345988
13.6%
. 345988
13.6%
1 198799
 
7.8%
5 170454
 
6.7%
2 164405
 
6.5%
4 133584
 
5.3%
9 131910
 
5.2%
3 131539
 
5.2%
6 122750
 
4.8%
Other values (3) 230917
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2536542
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 560208
22.1%
$ 345988
13.6%
. 345988
13.6%
1 198799
 
7.8%
5 170454
 
6.7%
2 164405
 
6.5%
4 133584
 
5.3%
9 131910
 
5.2%
3 131539
 
5.2%
6 122750
 
4.8%
Other values (3) 230917
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2536542
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 560208
22.1%
$ 345988
13.6%
. 345988
13.6%
1 198799
 
7.8%
5 170454
 
6.7%
2 164405
 
6.5%
4 133584
 
5.3%
9 131910
 
5.2%
3 131539
 
5.2%
6 122750
 
4.8%
Other values (3) 230917
9.1%
Distinct150305
Distinct (%)43.4%
Missing30
Missing (%)< 0.1%
Memory size2.6 MiB
2024-12-15T15:20:08.712636image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length14
Median length13
Mean length7.857391
Min length5

Characters and Unicode

Total characters2718563
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique114575 ?
Unique (%)33.1%

Sample

1st row$1.00
2nd row$4.00
3rd row$675.00
4th row$6080.26
5th row$1362.00
ValueCountFrequency (%)
0.00 7519
 
2.2%
10000.00 2292
 
0.7%
50000.00 1265
 
0.4%
15000.00 1153
 
0.3%
20000.00 1008
 
0.3%
30000.00 1005
 
0.3%
25000.00 969
 
0.3%
40000.00 696
 
0.2%
5000.00 685
 
0.2%
4999.00 616
 
0.2%
Other values (149654) 328780
95.0%
2024-12-15T15:20:09.695150image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 653196
24.0%
$ 345988
12.7%
. 345988
12.7%
1 200334
 
7.4%
2 180568
 
6.6%
5 176179
 
6.5%
4 151616
 
5.6%
6 140811
 
5.2%
3 134785
 
5.0%
8 134106
 
4.9%
Other values (3) 254992
 
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2718563
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 653196
24.0%
$ 345988
12.7%
. 345988
12.7%
1 200334
 
7.4%
2 180568
 
6.6%
5 176179
 
6.5%
4 151616
 
5.6%
6 140811
 
5.2%
3 134785
 
5.0%
8 134106
 
4.9%
Other values (3) 254992
 
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2718563
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 653196
24.0%
$ 345988
12.7%
. 345988
12.7%
1 200334
 
7.4%
2 180568
 
6.6%
5 176179
 
6.5%
4 151616
 
5.6%
6 140811
 
5.2%
3 134785
 
5.0%
8 134106
 
4.9%
Other values (3) 254992
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2718563
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 653196
24.0%
$ 345988
12.7%
. 345988
12.7%
1 200334
 
7.4%
2 180568
 
6.6%
5 176179
 
6.5%
4 151616
 
5.6%
6 140811
 
5.2%
3 134785
 
5.0%
8 134106
 
4.9%
Other values (3) 254992
 
9.4%
Distinct46049
Distinct (%)13.3%
Missing1017
Missing (%)0.3%
Memory size2.6 MiB
2024-12-15T15:20:10.073659image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length255
Median length8
Mean length13.160913
Min length6

Characters and Unicode

Total characters4540528
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30678 ?
Unique (%)8.9%

Sample

1st row76121504
2nd row44103127
3rd row44103127
4th row85121615
5th row44103127
ValueCountFrequency (%)
15101506 13313
 
2.5%
44103103 7628
 
1.4%
86101605 4802
 
0.9%
85101705 4297
 
0.8%
81112201 4151
 
0.8%
80121903 3680
 
0.7%
43211503 2807
 
0.5%
44101501 2611
 
0.5%
43211507 2558
 
0.5%
56101504 2551
 
0.5%
Other values (16967) 494789
91.1%
2024-12-15T15:20:10.644414image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1288099
28.4%
0 697883
15.4%
2 537533
11.8%
4 403306
 
8.9%
5 403095
 
8.9%
3 292989
 
6.5%
6 229673
 
5.1%
7 203444
 
4.5%
198186
 
4.4%
8 179319
 
3.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4540528
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1288099
28.4%
0 697883
15.4%
2 537533
11.8%
4 403306
 
8.9%
5 403095
 
8.9%
3 292989
 
6.5%
6 229673
 
5.1%
7 203444
 
4.5%
198186
 
4.4%
8 179319
 
3.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4540528
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1288099
28.4%
0 697883
15.4%
2 537533
11.8%
4 403306
 
8.9%
5 403095
 
8.9%
3 292989
 
6.5%
6 229673
 
5.1%
7 203444
 
4.5%
198186
 
4.4%
8 179319
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4540528
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1288099
28.4%
0 697883
15.4%
2 537533
11.8%
4 403306
 
8.9%
5 403095
 
8.9%
3 292989
 
6.5%
6 229673
 
5.1%
7 203444
 
4.5%
198186
 
4.4%
8 179319
 
3.9%

Normalized UNSPSC
Real number (ℝ)

Distinct13553
Distinct (%)3.9%
Missing1017
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean49321974
Minimum301817
Maximum95141903
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2024-12-15T15:20:10.827057image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum301817
5-th percentile14111703
Q139111712
median44121604
Q372101506
95-th percentile86101705
Maximum95141903
Range94840086
Interquartile range (IQR)32989794

Descriptive statistics

Standard deviation22469666
Coefficient of variation (CV)0.4555711
Kurtosis-0.80910019
Mean49321974
Median Absolute Deviation (MAD)12959201
Skewness0.27843798
Sum1.701613 × 1013
Variance5.048859 × 1014
MonotonicityNot monotonic
2024-12-15T15:20:11.000370image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15101506 12776
 
3.7%
44103103 7274
 
2.1%
86101605 4783
 
1.4%
85101705 4204
 
1.2%
81112201 3745
 
1.1%
80121903 3625
 
1.0%
43211503 2609
 
0.8%
44101501 2578
 
0.7%
14111507 2456
 
0.7%
56101504 2426
 
0.7%
Other values (13543) 298525
86.3%
ValueCountFrequency (%)
301817 6
 
< 0.1%
401723 2
 
< 0.1%
401726 2
 
< 0.1%
401728 2
 
< 0.1%
401729 1
 
< 0.1%
401733 4
 
< 0.1%
3018173 1
 
< 0.1%
10101501 1
 
< 0.1%
10101502 29
< 0.1%
10101506 39
< 0.1%
ValueCountFrequency (%)
95141903 289
0.1%
95141902 2
 
< 0.1%
95141901 4
 
< 0.1%
95141803 26
 
< 0.1%
95141802 42
 
< 0.1%
95141801 12
 
< 0.1%
95141710 1
 
< 0.1%
95141708 2
 
< 0.1%
95141707 1
 
< 0.1%
95141706 4
 
< 0.1%
Distinct13220
Distinct (%)3.9%
Missing3295
Missing (%)1.0%
Memory size2.6 MiB
2024-12-15T15:20:11.326123image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length119
Median length83
Mean length24.193153
Min length2

Characters and Unicode

Total characters8291550
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3486 ?
Unique (%)1.0%

Sample

1st rowJalapeno peppers
2nd rowIda red apples
3rd rowMagazines
4th rowHay
5th rowCoffee
ValueCountFrequency (%)
or 113917
 
9.9%
services 35130
 
3.1%
maintenance 15546
 
1.4%
service 14114
 
1.2%
gasoline 12806
 
1.1%
petrol 12776
 
1.1%
software 11858
 
1.0%
training 11688
 
1.0%
printer 11615
 
1.0%
medical 10148
 
0.9%
Other values (8077) 900650
78.3%
2024-12-15T15:20:11.938373image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 903605
10.9%
807525
 
9.7%
r 683227
 
8.2%
s 612508
 
7.4%
i 605481
 
7.3%
o 561156
 
6.8%
a 537448
 
6.5%
t 525956
 
6.3%
n 487626
 
5.9%
c 353554
 
4.3%
Other values (64) 2213464
26.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8291550
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 903605
10.9%
807525
 
9.7%
r 683227
 
8.2%
s 612508
 
7.4%
i 605481
 
7.3%
o 561156
 
6.8%
a 537448
 
6.5%
t 525956
 
6.3%
n 487626
 
5.9%
c 353554
 
4.3%
Other values (64) 2213464
26.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8291550
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 903605
10.9%
807525
 
9.7%
r 683227
 
8.2%
s 612508
 
7.4%
i 605481
 
7.3%
o 561156
 
6.8%
a 537448
 
6.5%
t 525956
 
6.3%
n 487626
 
5.9%
c 353554
 
4.3%
Other values (64) 2213464
26.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8291550
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 903605
10.9%
807525
 
9.7%
r 683227
 
8.2%
s 612508
 
7.4%
i 605481
 
7.3%
o 561156
 
6.8%
a 537448
 
6.5%
t 525956
 
6.3%
n 487626
 
5.9%
c 353554
 
4.3%
Other values (64) 2213464
26.7%

Class
Real number (ℝ)

Distinct2363
Distinct (%)0.7%
Missing3295
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean49276328
Minimum10101500
Maximum95141900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2024-12-15T15:20:12.107617image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum10101500
5-th percentile14111700
Q139111600
median44121500
Q371161100
95-th percentile86101700
Maximum95141900
Range85040400
Interquartile range (IQR)32049500

Descriptive statistics

Standard deviation22461054
Coefficient of variation (CV)0.45581834
Kurtosis-0.80593963
Mean49276328
Median Absolute Deviation (MAD)12959100
Skewness0.28204864
Sum1.6888131 × 1013
Variance5.0449894 × 1014
MonotonicityNot monotonic
2024-12-15T15:20:12.288611image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15101500 14584
 
4.2%
44103100 9457
 
2.7%
14111500 7190
 
2.1%
81112200 7026
 
2.0%
43211500 6974
 
2.0%
86101600 4951
 
1.4%
85101700 4478
 
1.3%
76122400 4458
 
1.3%
81111800 4122
 
1.2%
56101500 3980
 
1.2%
Other values (2353) 275503
79.6%
ValueCountFrequency (%)
10101500 100
 
< 0.1%
10101600 21
 
< 0.1%
10101700 89
 
< 0.1%
10101800 7
 
< 0.1%
10101900 2
 
< 0.1%
10111300 18
 
< 0.1%
10121500 95
 
< 0.1%
10121600 119
 
< 0.1%
10121700 662
0.2%
10121800 12
 
< 0.1%
ValueCountFrequency (%)
95141900 295
0.1%
95141800 80
 
< 0.1%
95141700 59
 
< 0.1%
95141600 10
 
< 0.1%
95141500 2
 
< 0.1%
95131700 26
 
< 0.1%
95131600 134
< 0.1%
95131500 4
 
< 0.1%
95122700 6
 
< 0.1%
95122500 4
 
< 0.1%
Distinct2360
Distinct (%)0.7%
Missing3295
Missing (%)1.0%
Memory size2.6 MiB
2024-12-15T15:20:12.599116image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length100
Median length79
Mean length28.290865
Min length3

Characters and Unicode

Total characters9695930
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique278 ?
Unique (%)0.1%

Sample

1st rowPeppers
2nd rowApples
3rd rowPrinted publications
4th rowLivestock feed
5th rowCoffee and tea
ValueCountFrequency (%)
and 216625
 
17.3%
services 44999
 
3.6%
accessories 25852
 
2.1%
equipment 22218
 
1.8%
supplies 20819
 
1.7%
software 15783
 
1.3%
support 15257
 
1.2%
products 14812
 
1.2%
petroleum 14588
 
1.2%
distillates 14584
 
1.2%
Other values (2496) 844531
67.6%
2024-12-15T15:20:13.167465image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 967534
 
10.0%
907345
 
9.4%
i 773464
 
8.0%
n 770503
 
7.9%
a 760879
 
7.8%
s 752611
 
7.8%
t 633816
 
6.5%
r 609543
 
6.3%
o 489272
 
5.0%
c 426257
 
4.4%
Other values (44) 2604706
26.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9695930
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 967534
 
10.0%
907345
 
9.4%
i 773464
 
8.0%
n 770503
 
7.9%
a 760879
 
7.8%
s 752611
 
7.8%
t 633816
 
6.5%
r 609543
 
6.3%
o 489272
 
5.0%
c 426257
 
4.4%
Other values (44) 2604706
26.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9695930
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 967534
 
10.0%
907345
 
9.4%
i 773464
 
8.0%
n 770503
 
7.9%
a 760879
 
7.8%
s 752611
 
7.8%
t 633816
 
6.5%
r 609543
 
6.3%
o 489272
 
5.0%
c 426257
 
4.4%
Other values (44) 2604706
26.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9695930
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 967534
 
10.0%
907345
 
9.4%
i 773464
 
8.0%
n 770503
 
7.9%
a 760879
 
7.8%
s 752611
 
7.8%
t 633816
 
6.5%
r 609543
 
6.3%
o 489272
 
5.0%
c 426257
 
4.4%
Other values (44) 2604706
26.9%

Family
Real number (ℝ)

Distinct409
Distinct (%)0.1%
Missing3295
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean49272189
Minimum3018000
Maximum95140000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2024-12-15T15:20:13.366356image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum3018000
5-th percentile14110000
Q139110000
median44120000
Q371160000
95-th percentile86100000
Maximum95140000
Range92122000
Interquartile range (IQR)32050000

Descriptive statistics

Standard deviation22464295
Coefficient of variation (CV)0.45592241
Kurtosis-0.80566174
Mean49272189
Median Absolute Deviation (MAD)12960000
Skewness0.28158343
Sum1.6886712 × 1013
Variance5.0464456 × 1014
MonotonicityNot monotonic
2024-12-15T15:20:13.581729image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44100000 16479
 
4.8%
15100000 14599
 
4.2%
81110000 13946
 
4.0%
43210000 13851
 
4.0%
14110000 10772
 
3.1%
86100000 9785
 
2.8%
44120000 9342
 
2.7%
43230000 8274
 
2.4%
76120000 5433
 
1.6%
56100000 5177
 
1.5%
Other values (399) 235065
67.9%
ValueCountFrequency (%)
3018000 28
 
< 0.1%
10100000 219
 
0.1%
10110000 18
 
< 0.1%
10120000 891
0.3%
10130000 53
 
< 0.1%
10140000 247
 
0.1%
10150000 691
0.2%
10160000 66
 
< 0.1%
10170000 725
0.2%
10190000 308
 
0.1%
ValueCountFrequency (%)
95140000 446
0.1%
95130000 164
 
< 0.1%
95120000 315
0.1%
95110000 14
 
< 0.1%
95100000 14
 
< 0.1%
94130000 726
0.2%
94120000 65
 
< 0.1%
94110000 3
 
< 0.1%
94100000 123
 
< 0.1%
93170000 5
 
< 0.1%
Distinct411
Distinct (%)0.1%
Missing3295
Missing (%)1.0%
Memory size2.6 MiB
2024-12-15T15:20:13.929837image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length87
Median length60
Mean length29.268721
Min length5

Characters and Unicode

Total characters10031064
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowFresh vegetables
2nd rowFresh fruits
3rd rowPrinted media
4th rowAnimal feed
5th rowBeverages
ValueCountFrequency (%)
and 254171
 
19.6%
services 54130
 
4.2%
accessories 49177
 
3.8%
equipment 44553
 
3.4%
supplies 43741
 
3.4%
products 34703
 
2.7%
computer 27797
 
2.1%
office 27745
 
2.1%
or 21073
 
1.6%
machines 16651
 
1.3%
Other values (634) 725216
55.8%
2024-12-15T15:20:14.411956image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1023173
 
10.2%
956234
 
9.5%
a 788625
 
7.9%
i 775602
 
7.7%
s 774769
 
7.7%
n 773716
 
7.7%
r 630418
 
6.3%
t 600726
 
6.0%
o 505177
 
5.0%
c 470489
 
4.7%
Other values (39) 2732135
27.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10031064
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1023173
 
10.2%
956234
 
9.5%
a 788625
 
7.9%
i 775602
 
7.7%
s 774769
 
7.7%
n 773716
 
7.7%
r 630418
 
6.3%
t 600726
 
6.0%
o 505177
 
5.0%
c 470489
 
4.7%
Other values (39) 2732135
27.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10031064
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1023173
 
10.2%
956234
 
9.5%
a 788625
 
7.9%
i 775602
 
7.7%
s 774769
 
7.7%
n 773716
 
7.7%
r 630418
 
6.3%
t 600726
 
6.0%
o 505177
 
5.0%
c 470489
 
4.7%
Other values (39) 2732135
27.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10031064
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1023173
 
10.2%
956234
 
9.5%
a 788625
 
7.9%
i 775602
 
7.7%
s 774769
 
7.7%
n 773716
 
7.7%
r 630418
 
6.3%
t 600726
 
6.0%
o 505177
 
5.0%
c 470489
 
4.7%
Other values (39) 2732135
27.2%

Segment
Real number (ℝ)

Distinct56
Distinct (%)< 0.1%
Missing3295
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean49132830
Minimum10000000
Maximum95000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 MiB
2024-12-15T15:20:14.629279image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Quantile statistics

Minimum10000000
5-th percentile14000000
Q139000000
median44000000
Q371000000
95-th percentile86000000
Maximum95000000
Range85000000
Interquartile range (IQR)32000000

Descriptive statistics

Standard deviation22466364
Coefficient of variation (CV)0.45725767
Kurtosis-0.80651688
Mean49132830
Median Absolute Deviation (MAD)13000000
Skewness0.28463721
Sum1.6838951 × 1013
Variance5.0473749 × 1014
MonotonicityNot monotonic
2024-12-15T15:20:14.815659image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43000000 32681
 
9.4%
50000000 27875
 
8.1%
44000000 27745
 
8.0%
81000000 17350
 
5.0%
42000000 16491
 
4.8%
15000000 16029
 
4.6%
14000000 11235
 
3.2%
25000000 10965
 
3.2%
86000000 10643
 
3.1%
46000000 10196
 
2.9%
Other values (46) 161513
46.7%
ValueCountFrequency (%)
10000000 3299
 
1.0%
11000000 2203
 
0.6%
12000000 2402
 
0.7%
13000000 504
 
0.1%
14000000 11235
3.2%
15000000 16029
4.6%
20000000 3060
 
0.9%
21000000 548
 
0.2%
22000000 1372
 
0.4%
23000000 2920
 
0.8%
ValueCountFrequency (%)
95000000 953
 
0.3%
94000000 917
 
0.3%
93000000 5724
1.7%
92000000 2168
 
0.6%
91000000 255
 
0.1%
90000000 1515
 
0.4%
86000000 10643
3.1%
85000000 7977
2.3%
84000000 1236
 
0.4%
83000000 1733
 
0.5%
Distinct56
Distinct (%)< 0.1%
Missing3295
Missing (%)1.0%
Memory size2.6 MiB
2024-12-15T15:20:15.190806image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length127
Median length70
Mean length51.28523
Min length18

Characters and Unicode

Total characters17576628
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFood Beverage and Tobacco Products
2nd rowFood Beverage and Tobacco Products
3rd rowPublished Products
4th rowLive Plant and Animal Material and Accessories and Supplies
5th rowFood Beverage and Tobacco Products
ValueCountFrequency (%)
and 674964
29.9%
supplies 100666
 
4.5%
accessories 89765
 
4.0%
services 86795
 
3.8%
equipment 80191
 
3.6%
products 54558
 
2.4%
technology 50031
 
2.2%
materials 36415
 
1.6%
components 35958
 
1.6%
information 32681
 
1.4%
Other values (144) 1014922
45.0%
2024-12-15T15:20:15.713026image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1914223
 
10.9%
n 1640863
 
9.3%
e 1481553
 
8.4%
a 1447141
 
8.2%
i 1311748
 
7.5%
s 1050829
 
6.0%
d 954285
 
5.4%
o 869130
 
4.9%
r 865549
 
4.9%
c 845799
 
4.8%
Other values (36) 5195508
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17576628
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1914223
 
10.9%
n 1640863
 
9.3%
e 1481553
 
8.4%
a 1447141
 
8.2%
i 1311748
 
7.5%
s 1050829
 
6.0%
d 954285
 
5.4%
o 869130
 
4.9%
r 865549
 
4.9%
c 845799
 
4.8%
Other values (36) 5195508
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17576628
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1914223
 
10.9%
n 1640863
 
9.3%
e 1481553
 
8.4%
a 1447141
 
8.2%
i 1311748
 
7.5%
s 1050829
 
6.0%
d 954285
 
5.4%
o 869130
 
4.9%
r 865549
 
4.9%
c 845799
 
4.8%
Other values (36) 5195508
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17576628
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1914223
 
10.9%
n 1640863
 
9.3%
e 1481553
 
8.4%
a 1447141
 
8.2%
i 1311748
 
7.5%
s 1050829
 
6.0%
d 954285
 
5.4%
o 869130
 
4.9%
r 865549
 
4.9%
c 845799
 
4.8%
Other values (36) 5195508
29.6%

Location
Text

Missing 

Distinct3993
Distinct (%)1.4%
Missing70110
Missing (%)20.3%
Memory size2.6 MiB
2024-12-15T15:20:16.117639image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Length

Max length35
Median length30
Mean length29.4052
Min length4

Characters and Unicode

Total characters8113130
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique742 ?
Unique (%)0.3%

Sample

1st row95841 (38.662263, -121.346136)
2nd row91436 (34.151642, -118.49051)
3rd row95814 (38.580427, -121.494396)
4th row97008 (45.460518, -122.806409)
5th row95814 (38.580427, -121.494396)
ValueCountFrequency (%)
38.575311 11643
 
1.4%
121.560401 11643
 
1.4%
38.580427 11510
 
1.4%
121.494396 11510
 
1.4%
95691 11095
 
1.4%
95814 10921
 
1.3%
122.02 8518
 
1.0%
95696 8518
 
1.0%
38.43 8518
 
1.0%
121.328511 7159
 
0.9%
Other values (10596) 720425
87.7%
2024-12-15T15:20:16.754639image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 936712
11.5%
3 712585
 
8.8%
9 620962
 
7.7%
2 608096
 
7.5%
5 560846
 
6.9%
8 556014
 
6.9%
. 545412
 
6.7%
4 523946
 
6.5%
7 502781
 
6.2%
6 483033
 
6.0%
Other values (36) 2062743
25.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8113130
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 936712
11.5%
3 712585
 
8.8%
9 620962
 
7.7%
2 608096
 
7.5%
5 560846
 
6.9%
8 556014
 
6.9%
. 545412
 
6.7%
4 523946
 
6.5%
7 502781
 
6.2%
6 483033
 
6.0%
Other values (36) 2062743
25.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8113130
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 936712
11.5%
3 712585
 
8.8%
9 620962
 
7.7%
2 608096
 
7.5%
5 560846
 
6.9%
8 556014
 
6.9%
. 545412
 
6.7%
4 523946
 
6.5%
7 502781
 
6.2%
6 483033
 
6.0%
Other values (36) 2062743
25.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8113130
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 936712
11.5%
3 712585
 
8.8%
9 620962
 
7.7%
2 608096
 
7.5%
5 560846
 
6.9%
8 556014
 
6.9%
. 545412
 
6.7%
4 523946
 
6.5%
7 502781
 
6.2%
6 483033
 
6.0%
Other values (36) 2062743
25.4%

Interactions

2024-12-15T15:19:47.466487image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:41.764465image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:42.887426image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:44.004656image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:45.113745image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:46.345196image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:47.655465image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:41.991466image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:43.103424image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:44.175688image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:45.286596image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:46.522194image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:47.866469image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:42.182333image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:43.299455image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:44.359699image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:45.513824image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:46.735788image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:48.072930image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:42.353334image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:43.476327image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:44.557135image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:45.777119image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:46.914785image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:48.237746image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:42.527256image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:43.649321image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:44.723839image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:45.955196image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:47.120390image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:48.400608image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:42.697702image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:43.827567image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:44.928835image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:46.157206image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
2024-12-15T15:19:47.304389image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/

Missing values

2024-12-15T15:19:48.919088image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-15T15:19:50.396629image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-12-15T15:19:53.366426image/svg+xmlMatplotlib v3.9.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Creation DatePurchase DateFiscal YearLPA NumberPurchase Order NumberRequisition NumberAcquisition TypeSub-Acquisition TypeAcquisition MethodSub-Acquisition MethodDepartment NameSupplier CodeSupplier NameSupplier QualificationsSupplier Zip CodeCalCardItem NameItem DescriptionQuantityUnit PriceTotal PriceClassification CodesNormalized UNSPSCCommodity TitleClassClass TitleFamilyFamily TitleSegmentSegment TitleLocation
008/27/2013NaN2013-20147-12-70-26REQ0011118REQ0011118IT GoodsNaNWSCA/CoopNaNConsumer Affairs, Department of1740272.0Pitney BowesNaNNaNNOUSBUSB1.0$1.00$1.00NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
101/29/2014NaN2013-2014NaNREQ0011932REQ0011932NON-IT GoodsNaNInformal CompetitiveNaNConsumer Affairs, Department of1760085.0Rodea Auto TechNaNNaNNOTire DisposalTire Disposal2.0$2.00$4.007612150476121504.0NaNNaNNaNNaNNaNNaNNaNNaN
211/01/2013NaN2013-2014NaNREQ0011476REQ0011476IT ServicesNaNInformal CompetitiveNaNConsumer Affairs, Department of17224.0Smile Business Products, IncNaN95841NOLaborLabor4.5$150.00$675.00NaNNaNNaNNaNNaNNaNNaNNaNNaN95841\n(38.662263, -121.346136)
306/13/201406/05/20142013-2014NaN4500236642NaNNON-IT GoodsNaNInformal CompetitiveNaNCorrectional Health Care Services1754462.0ASHAN INCCA-MB CA-SB91436NONaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN91436\n(34.151642, -118.49051)
403/12/201403/12/20142013-20141-10-75-60A4500221028NaNNON-IT GoodsNaNStatewide ContractNaNCorrections and Rehabilitation, Department of1087660.0Technology Integration GroupNaN95814NOTonerToner1.0$6080.26$6080.264410312744103127.0NaNNaNNaNNaNNaNNaNNaN95814\n(38.580427, -121.494396)
510/09/201410/01/20142014-2015NaN4500253427NaNNON-IT GoodsNaNInformal CompetitiveNaNCorrectional Health Care Services1738777.0WALGREENS SPECIALTY PHARMACY LLCNaN97008NONaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN97008\n(45.460518, -122.806409)
610/10/2014NaN2014-20151-14-75-60AREQ0013911REQ0013911NON-IT GoodsNaNStatewide ContractNaNConsumer Affairs, Department of1087660.0Technology Integration GroupNaN95814NOHP 35A BLACK TONERHP 35A BLACK TONER30.0$45.40$1362.004410312744103127.0NaNNaNNaNNaNNaNNaNNaN95814\n(38.580427, -121.494396)
704/24/201404/14/20142013-2014NaN12-64006.01NaNNON-IT ServicesPersonal ServicesServices are specifically exempt by statuteNaNState Hospitals, Department of1069280.0David GallardoNaN93274NORadiation OncologyRadiation Oncology1.0$400000.00$400000.008512161585121615.0NaNNaNNaNNaNNaNNaNNaN93274\n(36.193481, -119.358379)
802/06/2015NaN2014-20151-14-75-60AREQ0014515REQ0014515NON-IT GoodsNaNStatewide ContractNaNConsumer Affairs, Department of1087660.0Technology Integration GroupNaN95814NOBlack Toner Cartridge for CLJ 4700Black Toner Cartridge for CLJ 470016.0$127.06$2032.964410312744103127.0NaNNaNNaNNaNNaNNaNNaN95814\n(38.580427, -121.494396)
908/14/201307/26/20132013-2014NaN4500200308NaNNON-IT GoodsNaNInformal CompetitiveNaNWater Resources, Department of1014234.0CLARKE SALESNaN91322NO1" ss 90* elbow , threaded1" ss 90* elbow , threaded4.0$21.65$86.60401728401728.0NaNNaNNaNNaNNaNNaNNaN91322\n(34.379263, -118.547301)
Creation DatePurchase DateFiscal YearLPA NumberPurchase Order NumberRequisition NumberAcquisition TypeSub-Acquisition TypeAcquisition MethodSub-Acquisition MethodDepartment NameSupplier CodeSupplier NameSupplier QualificationsSupplier Zip CodeCalCardItem NameItem DescriptionQuantityUnit PriceTotal PriceClassification CodesNormalized UNSPSCCommodity TitleClassClass TitleFamilyFamily TitleSegmentSegment TitleLocation
34600811/28/2012NaN2012-2013NaN1026NaNNON-IT GoodsNaNNCBNaNHighway Patrol, California1732038.0Commercial Vehicle Safety AllianceNaN20770NOCommercial Vehicle Safety Alliance (CVSA) Decals - Orange, 3rd Qtr. of 2013For delivery in 3rd qtr. of 2013 (Jul, Aug, Sep)135000.0$0.28$37800.005512160755121607.0Decals55121600.0Labels55120000.0Signage and accessories55000000.0Published Products20770\n(39.000196, -76.882952)
34600905/10/201307/01/20132012-2013NaN3169558NaNNON-IT ServicesLegal ServicesServices are specifically exempt by statuteNaNGeneral Services, Department of1135461.0Michael DilibertoNaN90067NOSE Pro Tem ServicesContractor will provide services as a Pro Tem ALJ during Special\nEducation hearings and mediations.1.0$25000.00$25000.009315150793151507.0Administrative procedures or services93151500.0Public administration93150000.0Public administration and finance services93000000.0Politics and Civic Affairs Services90067\n(34.057398, -118.414336)
34601006/17/201406/05/20142013-2014NaNP1475034NaNNON-IT ServicesNonprofit OrganizationsInformal CompetitiveNaNFish and Wildlife, Department of1086212.0Marine Exchange of Southern CaliforniaNaN90733NOGrantHarbor Safety Committee Secretariat Services1.0$51268.60$51268.607713150277131502.0Oil spillage control services77131500.0Oil pollution77130000.0Pollutants tracking and monitoring and rehabilitation services77000000.0Environmental Services90733\n(33.735802, -118.291397)
34601107/30/201307/30/20132013-20141-11-70-04O4500192139NaNIT ServicesNaNStatewide ContractNaNCorrections and Rehabilitation, Department of17224.0Smile Business Products, IncNaN95841NOCopier MaintenanceCopier Maintenance8.0$2600.00$20800.008111220181112201.0Maintenance or support fees81112200.0Software maintenance and support81110000.0Computer services81000000.0Engineering and Research and Technology Based Services95841\n(38.662263, -121.346136)
34601212/31/201312/31/20132013-2014NaNP1300585NaNNON-IT GoodsNaNFair and ReasonableNaNFranchise Tax Board59158.0Flash Business SolutionsCA-MB CA-SB95763-1046NOformsReport of Deposit forms1.0$2548.80$2548.801411180614111806.0Business forms or questionnaires14111800.0Business use papers14110000.0Paper products14000000.0Paper Materials and Products95763-1046\n(38.681325, -121.163738)
34601311/03/201409/25/20142014-2015NaN4500252536NaNNON-IT GoodsNaNInformal CompetitiveNaNCorrections and Rehabilitation, Department of12205.0Superior Produce, Inc.CA-SB95811NOInmate feeding: Oct. produce order, 2nd quar.Inmate feeding: Oct. produce order, 2nd quar.1.0$37479.45$37479.459313160893131608.0Food supply services93131600.0Food and nutrition policy planning and programs93130000.0Humanitarian aid and relief93000000.0Politics and Civic Affairs Services95811\n(38.581053, -121.488564)
34601408/14/201407/20/20112014-2015NaN10A1343NaNNON-IT ServicesExpert WitnesesServices are specifically exempt by statuteNaNTransportation, Department of1018638.0CONFIDENTIAL - Information WithheldNaN95816NOExpert WitnessExpert Witness Real Estate Appraisals - Amendment for additional funds.1.0$35000.00$35000.008012190380121903.0Expert witness service80121900.0Compensated legal participation services80120000.0Legal services80000000.0Management and Business Professionals and Administrative Services95816\n(38.57219, -121.467691)
34601505/09/2014NaN2013-2014NaN228NaNIT GoodsNaNSB/DVBE OptionNaNHigh Speed Rail Authority, California1136849.0Straight-Line SolutionsCA-MB CA-SB95829NOInout Panel CoverProjector accessories2.0$49.99$99.9845111609\n5610171045111609.0Multimedia projectors45111600.0Projectors and supplies45110000.0Audio and visual presentation and composing equipment45000000.0Printing and Photographic and Audio and Visual Equipment and Supplies95829\n(38.474725, -121.340819)
34601601/14/201401/14/20142013-2014NaNP1300614NaNNON-IT GoodsNaNFair and ReasonableNaNFranchise Tax Board8329.0Merritt Business SuppliesCA-MB CA-SB95630NODremel 120 Volt EngraverDremel 120 Volt Engraver1.0$35.00$35.002711271827112718.0Engravers27112700.0Power tools27110000.0Hand tools27000000.0Tools and General Machinery95630\n(38.670213, -121.147592)
34601710/09/201309/09/20132013-2014NaN2ui3L262NaNNON-IT ServicesContracts with Local GovernmentsEmergency PurchaseNaNForestry and Fire Protection, Department of1050481.0Happy Valley Fire Protection DistrictNaN96007NOLOCAL GOVERNMENT-LABORLocal government engines on going fire "Clover"1.0$17455.91$17455.917612240576122405.0Labor fee76122400.0Refuse disposal and treatment fees76120000.0Refuse disposal and treatment76000000.0Industrial Cleaning Services96007\n(40.456408, -122.315294)

Duplicate rows

Most frequently occurring

Creation DatePurchase DateFiscal YearLPA NumberPurchase Order NumberRequisition NumberAcquisition TypeSub-Acquisition TypeAcquisition MethodSub-Acquisition MethodDepartment NameSupplier CodeSupplier NameSupplier QualificationsSupplier Zip CodeCalCardItem NameItem DescriptionQuantityUnit PriceTotal PriceClassification CodesNormalized UNSPSCCommodity TitleClassClass TitleFamilyFamily TitleSegmentSegment TitleLocation# duplicates
40904/21/201501/29/20152014-20155-06-58-20CF140541NaNIT ServicesNaNMaster Service AgreementNaNForestry and Fire Protection, Department of1000530.0AT&TNaN95134NOUPS Network Management Card 2Network Maintenance1.0$512.55$512.558111180481111804.0Wide area network WAN maintenance or support81111800.0System and system component administration services81110000.0Computer services81000000.0Engineering and Research and Technology Based Services95134\n(37.410635, -121.940949)25
40404/21/201501/29/20152014-20155-06-58-20CF140541NaNIT ServicesNaNMaster Service AgreementNaNForestry and Fire Protection, Department of1000530.0AT&TNaN95134NOTrustwave X100 ApplianceNetwork Maintenance1.0$4500.00$4500.008111180481111804.0Wide area network WAN maintenance or support81111800.0System and system component administration services81110000.0Computer services81000000.0Engineering and Research and Technology Based Services95134\n(37.410635, -121.940949)24
32604/08/201504/01/20152014-2015NaN4500221608NaNNON-IT GoodsNaNInformal CompetitiveNaNWater Resources, Department of35423.0Cal Best Industrial SupplyCA-MB CA-SB93308NOPANTS,ARC/FLASHPANTS,ARC/FLASH3.0$72.00$216.004618151846181518.0Heat resistant clothing46181500.0Safety apparel46180000.0Personal safety and protection46000000.0Defense and Law Enforcement and Security and Safety Equipment and Supplies93308\n(35.485441, -119.013391)22
37904/21/201501/29/20152014-20155-06-58-20CF140541NaNIT ServicesNaNMaster Service AgreementNaNForestry and Fire Protection, Department of1000530.0AT&TNaN95134NOAPC 2 Post Mounting Kit for Smart UPS and SymmetraNetwork Maintenance2.0$181.05$362.108111180481111804.0Wide area network WAN maintenance or support81111800.0System and system component administration services81110000.0Computer services81000000.0Engineering and Research and Technology Based Services95134\n(37.410635, -121.940949)22
39004/21/201501/29/20152014-20155-06-58-20CF140541NaNIT ServicesNaNMaster Service AgreementNaNForestry and Fire Protection, Department of1000530.0AT&TNaN95134NOCisco 3945 W/SPE 150Includes:\n Cisco AC Power Supply\n AC Power Cord\n Cisco Services Performance Engin 150 for Cisco\n IP Base License for Cisco\n Cisco Fan Assembly\n Cisco Config Pro Express on Router Flash\n 1GB DRAM\n 256 Compact Flash for Cisco\n Cisco IOS Un1.0$6500.00$6500.008111180481111804.0Wide area network WAN maintenance or support81111800.0System and system component administration services81110000.0Computer services81000000.0Engineering and Research and Technology Based Services95134\n(37.410635, -121.940949)22
38704/21/201501/29/20152014-20155-06-58-20CF140541NaNIT ServicesNaNMaster Service AgreementNaNForestry and Fire Protection, Department of1000530.0AT&TNaN95134NOAPC Smart UPS X2000VA Rack Tower LCdNetwork Maintenance1.0$1568.25$1568.258111180481111804.0Wide area network WAN maintenance or support81111800.0System and system component administration services81110000.0Computer services81000000.0Engineering and Research and Technology Based Services95134\n(37.410635, -121.940949)20
39604/21/201501/29/20152014-20155-06-58-20CF140541NaNIT ServicesNaNMaster Service AgreementNaNForestry and Fire Protection, Department of1000530.0AT&TNaN95134NOCradlepoint ARC CBA750B Wireless RouterNetwork Maintenance1.0$599.00$599.008111180481111804.0Wide area network WAN maintenance or support81111800.0System and system component administration services81110000.0Computer services81000000.0Engineering and Research and Technology Based Services95134\n(37.410635, -121.940949)17
40004/21/201501/29/20152014-20155-06-58-20CF140541NaNIT ServicesNaNMaster Service AgreementNaNForestry and Fire Protection, Department of1000530.0AT&TNaN95134NOOne Port T3 Network ModuleNetwork Maintenance1.0$4675.00$4675.008111180481111804.0Wide area network WAN maintenance or support81111800.0System and system component administration services81110000.0Computer services81000000.0Engineering and Research and Technology Based Services95134\n(37.410635, -121.940949)17
6301/10/201401/08/20142013-2014NaN4500206131NaNNON-IT ServicesAgreements with other governmental entities and public universitiesServices are specifically exempt by policyNaNWater Resources, Department of1117701.0South Coast Air Quality Management DistrictNaN91765NOAV AQMD FeeAV AQMD Fee1.0$224.29$224.299315151693151516.0Building permit93151500.0Public administration93150000.0Public administration and finance services93000000.0Politics and Civic Affairs Services91765\n(33.997935, -117.817436)16
10601/28/201501/27/20152014-2015NaN4500219416NaNNON-IT GoodsNaNInformal CompetitiveNaNWater Resources, Department of6523.0IBHP INCORPORATEDCA-SB98733NOPANTS,ARC/FLASHPANTS,ARC/FLASH2.0$51.50$103.004618151846181518.0Heat resistant clothing46181500.0Safety apparel46180000.0Personal safety and protection46000000.0Defense and Law Enforcement and Security and Safety Equipment and Supplies98733\n14